Instructions to use AI4Protein/deep_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI4Protein/deep_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AI4Protein/deep_base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AI4Protein/deep_base") model = AutoModelForMaskedLM.from_pretrained("AI4Protein/deep_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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pipeline_tag: feature-extraction
library_name: transformers
license: mit
---
# VenusFactory Protein Language Model
This model is part of the VenusFactory platform, described in [VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning](https://huggingface.co/papers/2503.15438). VenusFactory provides a unified platform for protein engineering data retrieval and language model fine-tuning, integrating many protein-related datasets and popular PLMs.
This specific model uses a masked language modeling objective for protein sequence feature extraction.
Code and further details are available at [https://github.com/tyang816/VenusFactory](https://github.com/tyang816/VenusFactory). |